
"""simple docstring"""


import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
      raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
      raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  """The Nymphenburg Palace is a beautiful palace in Munich!"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		{
           "attention_cell": "multi_head",
           "num_layers": 4,
           "units": 10_24,
           "hidden_size": 7_68,
           "max_length": 5_12,
           "num_heads": 8,
           "scaled": True,
           "dropout": 0.1,
           "use_residual": True,
           "embed_size": 10_24,
           "embed_dropout": 0.1,
           "word_embed": None,
           "layer_norm_eps": 1E-5,
           "token_type_vocab_size": 2,
       }

       UpperCamelCase       			=		bort_4_8_768_1024_hparams

       # Let's construct the original Bort model here
       # Taken from official BERT implementation, see:
       # https://github.com/alexa/bort/blob/master/bort/bort.py
       UpperCamelCase       			=		BERTEncoder(
           attention_cell=predefined_args["attention_cell"]		,   num_layers=predefined_args["num_layers"]		,   units=predefined_args["units"]		,   hidden_size=predefined_args["hidden_size"]		,   max_length=predefined_args["max_length"]		,   num_heads=predefined_args["num_heads"]		,   scaled=predefined_args["scaled"]		,   dropout=predefined_args["dropout"]		,   output_attention=UpperCAmelCase_		,   output_all_encodings=UpperCAmelCase_		,   use_residual=predefined_args["use_residual"]		,   activation=predefined_args.get("activation"		,   "gelu"					)		,   layer_norm_eps=predefined_args.get("layer_norm_eps"		,   UpperCAmelCase_					)		,   )

       # Vocab information needs to be fetched first
       # It's the same as RoBERTa, so RobertaTokenizer can be used later
       UpperCamelCase       			=		"openwebtext_ccnews_stories_books_cased"

       # Specify download folder to Gluonnlp's vocab
       UpperCamelCase       			=		os.path.join(get_home_dir()		,   "models"					)
       UpperCamelCase       			=		_load_vocab(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   cls=UpperCAmelCase_					)

       UpperCamelCase       			=		nlp.model.BERTModel(
           UpperCAmelCase_		,   len(UpperCAmelCase_					)		,   units=predefined_args["units"]		,   embed_size=predefined_args["embed_size"]		,   embed_dropout=predefined_args["embed_dropout"]		,   word_embed=predefined_args["word_embed"]		,   use_pooler=UpperCAmelCase_		,   use_token_type_embed=UpperCAmelCase_		,   token_type_vocab_size=predefined_args["token_type_vocab_size"]		,   use_classifier=UpperCAmelCase_		,   use_decoder=UpperCAmelCase_		,   )

       original_bort.load_parameters(UpperCAmelCase_		,   cast_dtype=UpperCAmelCase_		,   ignore_extra=UpperCAmelCase_					)
       UpperCamelCase       			=		original_bort._collect_params_with_prefix()

       # Build our config 🤗
       UpperCamelCase       			=		{
           "architectures": ["BertForMaskedLM"],
           "attention_probs_dropout_prob": predefined_args["dropout"],
           "hidden_act": "gelu",
           "hidden_dropout_prob": predefined_args["dropout"],
           "hidden_size": predefined_args["embed_size"],
           "initializer_range": 0.02,
           "intermediate_size": predefined_args["hidden_size"],
           "layer_norm_eps": predefined_args["layer_norm_eps"],
           "max_position_embeddings": predefined_args["max_length"],
           "model_type": "bort",
           "num_attention_heads": predefined_args["num_heads"],
           "num_hidden_layers": predefined_args["num_layers"],
           "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
           "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
           "vocab_size": len(UpperCAmelCase_					),
       }

       UpperCamelCase       			=		BertConfig.from_dict(UpperCAmelCase_					)
       UpperCamelCase       			=		BertForMaskedLM(UpperCAmelCase_					)
       hf_bort_model.eval()

       # Parameter mapping table (Gluonnlp to Transformers)
       # * denotes layer index
       #
       # | Gluon Parameter                                                | Transformers Parameter
       # | -------------------------------------------------------------- | ----------------------
       # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
       # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
       # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
       # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
       # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
       # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
       # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

       # Helper function to convert MXNET Arrays to PyTorch
       def to_torch(UpperCAmelCase_					) -> nn.Parameter:
              return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()					)					)

       # Check param shapes and map new HF param back
       def check_and_map_params(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		hf_param.shape

              UpperCamelCase       			=		to_torch(params[gluon_param]					)
              UpperCamelCase       			=		gluon_param.shape

              assert (
                  shape_hf == shape_gluon
              ), F"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"

              return gluon_param

       UpperCamelCase       			=		check_and_map_params(
           hf_bort_model.bert.embeddings.word_embeddings.weight		,   "word_embed.0.weight"					)
       UpperCamelCase       			=		check_and_map_params(
           hf_bort_model.bert.embeddings.position_embeddings.weight		,   "encoder.position_weight"					)
       UpperCamelCase       			=		check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.bias		,   "encoder.layer_norm.beta"					)
       UpperCamelCase       			=		check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.weight		,   "encoder.layer_norm.gamma"					)

       # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
       UpperCamelCase       			=		torch.zeros_like(
           hf_bort_model.bert.embeddings.token_type_embeddings.weight.data					)

       for i in range(hf_bort_config.num_hidden_layers					):
              UpperCamelCase       			=		hf_bort_model.bert.encoder.layer[i]

              # self attention
              UpperCamelCase       			=		layer.attention.self

              UpperCamelCase       			=		check_and_map_params(
                  self_attn.key.bias.data		,   F"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"					)

              UpperCamelCase       			=		check_and_map_params(
                  self_attn.key.weight.data		,   F"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_attn.query.bias.data		,   F"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_attn.query.weight.data		,   F"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_attn.value.bias.data		,   F"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_attn.value.weight.data		,   F"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"					)

              # self attention output
              UpperCamelCase       			=		layer.attention.output

              UpperCamelCase       			=		check_and_map_params(
                  self_output.dense.bias		,   F"encoder.transformer_cells.{i}.proj.bias"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_output.dense.weight		,   F"encoder.transformer_cells.{i}.proj.weight"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_output.LayerNorm.bias		,   F"encoder.transformer_cells.{i}.layer_norm.beta"					)
              UpperCamelCase       			=		check_and_map_params(
                  self_output.LayerNorm.weight		,   F"encoder.transformer_cells.{i}.layer_norm.gamma"					)

              # intermediate
              UpperCamelCase       			=		layer.intermediate

              UpperCamelCase       			=		check_and_map_params(
                  intermediate.dense.bias		,   F"encoder.transformer_cells.{i}.ffn.ffn_1.bias"					)
              UpperCamelCase       			=		check_and_map_params(
                  intermediate.dense.weight		,   F"encoder.transformer_cells.{i}.ffn.ffn_1.weight"					)

              # output
              UpperCamelCase       			=		layer.output

              UpperCamelCase       			=		check_and_map_params(
                  bert_output.dense.bias		,   F"encoder.transformer_cells.{i}.ffn.ffn_2.bias"					)
              UpperCamelCase       			=		check_and_map_params(
                  bert_output.dense.weight		,   F"encoder.transformer_cells.{i}.ffn.ffn_2.weight"					)
              UpperCamelCase       			=		check_and_map_params(
                  bert_output.LayerNorm.bias		,   F"encoder.transformer_cells.{i}.ffn.layer_norm.beta"					)
              UpperCamelCase       			=		check_and_map_params(
                  bert_output.LayerNorm.weight		,   F"encoder.transformer_cells.{i}.ffn.layer_norm.gamma"					)

       # Save space and energy 🎄
       hf_bort_model.half()

       # Compare output of both models
       UpperCamelCase       			=		RobertaTokenizer.from_pretrained("roberta-base"					)

       UpperCamelCase       			=		tokenizer.encode_plus(UpperCAmelCase_					)["input_ids"]

       # Get gluon output
       UpperCamelCase       			=		mx.nd.array([input_ids]					)
       UpperCamelCase       			=		original_bort(inputs=UpperCAmelCase_		,   token_types=[]					)

       # Get Transformer output (save and reload model again)
       hf_bort_model.save_pretrained(UpperCAmelCase_					)
       UpperCamelCase       			=		BertModel.from_pretrained(UpperCAmelCase_					)
       hf_bort_model.eval()

       UpperCamelCase       			=		tokenizer.encode_plus(UpperCAmelCase_		,   return_tensors="pt"					)
       UpperCamelCase       			=		hf_bort_model(**UpperCAmelCase_					)[0]

       UpperCamelCase       			=		output_gluon[0].asnumpy()
       UpperCamelCase       			=		output_hf[0].detach().numpy()

       UpperCamelCase       			=		np.max(np.abs(hf_layer - gluon_layer					)					).item()
       UpperCamelCase       			=		np.allclose(UpperCAmelCase_		,   UpperCAmelCase_		,   atol=1E-3					)

       if success:
              print("✔️ Both model do output the same tensors"					)
       else:
              print("❌ Both model do **NOT** output the same tensors"					)
              print("Absolute difference is:"		,   UpperCAmelCase_					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE			  =  parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





